code: def __init__(self):
      super().__init__()
      self.input_embedders_parameters = {
          "observation": InputEmbedderParameters()
      }
      self.middleware_parameters = FCMiddlewareParameters()
      self.heads_parameters = [
          QHeadParameters(loss_weight=0.5),
          ACERPolicyHeadParameters(loss_weight=1.0),
      ]
      self.optimizer_type = "Adam"
      self.async_training = True
      self.clip_gradients = 40.0
      self.create_target_network = True
  
imports: from typing import Union
  import numpy
  from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import ACERPolicyHeadParameters, QHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters
  from rl_coach.core_types import Batch
  from rl_coach.exploration_policies.categorical import CategoricalParameters
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.spaces import DiscreteActionSpace
  from rl_coach.utils import eps, last_sample
label_desc: Use image node_id 1 to create a new ACERNetworkParameters object from inherited base classes: NetworkParameters with example: obj = ACERNetworkParameters()